Informative soaring with drifting thermals

• Published in: 2016 IEEE International Conference on Robotics and Automation (ICRA) pp. 1522 - 1529
• Main Authors: Nguyen, Joseph L., Lawrance, Nicholas R. J., Fitch, Robert, Sukkarieh, Salah
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2016
• Subjects: Algorithm design and analysis; Clustering algorithms; Mathematical model; Probability density function; Schedules; Search problems
• DOI: 10.1109/ICRA.2016.7487289

The informative soaring (IFS) problem involves a gliding unmanned aerial vehicle (UAV) exploiting energy from thermals to extend its information gathering capability. In this paper, we address the realistic situation of detecting new thermals drifting with the wind in the search environment. We consider complex target-search scenarios characterised by information clusters and propose a new set of algorithms designed to both explore for and exploit high-value thermals to maximise information gain. Our algorithms: 1) compute a thermal exploration map to detect useful thermals that eventually intercept clusters, 2) solve a boundary value problem for inter-thermal path segment (ITP) generation with moving thermals, 3) compute thermal time windows to gather information from clusters and form a cluster service schedule, and 4) use branch and bound (BnB) tree search for global planning, considering high-utility-rate ITPs to maximise information gain. Our solution is compared against a greedy method that neither considers the thermal exploration map nor cluster schedule and a full knowledge method that has access to all thermals. Numerical simulations show that on average, our solution outperforms the greedy method in one-third of 2400 Monte Carlo trials, and achieves similar performance to the full knowledge method when environmental conditions are favourable.